The invention relates to registering multiple frames of two-dimensional image data.
Magnetic resonance imaging (MRI) is a widely used diagnostic technique where radio frequency (RF) signals are analyzed to produce diagnostic information. Echo-planar imaging (EPI) is a subset of MRI that provides high temporal resolution achieved with faster imaging techniques, and typically resulting in large image data sets. One application of EPI is functional magnetic resonance imaging (fMRI), where a time series of images is acquired for a selected plane (or planes) of a subject. In challenge-based fMRI of the human brain, a time series of images of one or more planes within a subject's brain is collected while the subject is exposed to a sequence of stimulus conditions, to identify functional changes in brain characteristics.
The high spatial resolution of EPI makes challenge-based experiments sensitive to subject movements on the scale of millimeters or less. Stimulus-correlated movements of the subject may lead to false results. The false information introduced by the subject's movement is commonly referred to as motion artifact. In order to analyze the time series of images, motion artifacts must be removed by registering the series of images. Proper inter-frame registration of the time series of images to remove motion artifact is particularly important in studies in which the subject's motion is an integral part of the experiment, such as experiments that require spoken responses or the performance of motor tasks.
Techniques used to register image sets include the use of physical immobilization devices to maintain the subject in a known position or the placement of external fiduciary markers as landmarks for post-processing alignment.
Approaches used after the image data is acquired include landmark matching, surface matching, brute force least-squares estimation, iterative least-squares estimation, and variations thereof. Landmark matching is a manual process that requires trained personnel to accurately identify landmarks in each image.
Surface matching techniques may be computationally intensive when applied to large fMRI time series data. Also, various post-processing techniques construct the "registered" image by linear interpolation of the "misregistered" image, a technique that may introduce aliasing noise and high-spatial-frequency attenuation into the final corrected image.